There are now two species of maned sloths threatened by extinction in Brazil, and they inhabit severely fragmented areas: Bradypus torquatus, in the states of Sergipe and Bahia, and Bradypus crinitus, in Rio de Janeiro and Espírito Santo. Because of these species' arboreal nature, it is difficult to find sloths in the forest and complex techniques must be used to capture them. In addition, the specific morphological and physiological characteristics of these species are seldom studied. There are several studies on the physiology and health of other species of the genus Bradypus, but there is a lack of scientific data related to the health of both B. torquatus and B. crinitus. Given the importance of blood tests for the assessment of animal health status of individuals and populations, this study presents the first hematological and biochemical values of the recently discovered species B. crinitus. In total, 19 clinically healthy maned sloths were captured and assessed (19 in total, maximum of 28 samples due to recaptures) for total leukocyte count, differential leukocyte count, RBC count, Hct, total solids, urea, creatinine, alkaline phosphatase, and alanine aminotransferase. These hematological and biochemistry values may help in assessing the health of free-living southern maned sloths.
Understanding the pathogenesis of amyloid- β pathology in Alzheimer's Disease (AD) proves to be a challenge. In this work, we expand upon the application of network diffusion models (NDM) to study pathophysiological spread of amyloid- β throughout white matter structural brain networks. We found that the NDM successfully recaptures subpopulation-level spatial patterns (Pearson's R=0.45-0.48, P FDR < 0.01) of amyloid- β deposition in the Alzheimer's Disease Neuroimaging Cohort at a regional level, but with drawbacks in mechanism interpretability. We then moved to an extended NDM framework (eNDM), including a protein synthesis term to better reflect the role of amyloid- β metabolism, as well as including regional vulnerability using spatial transcriptomics from the Allen Human Brain Atlas to modulate the region-level rate parameters of the synthesis term. The novel gene eNDMs exhibited significant performance increases in Pearson's correlation (Steiger's Z, P FDR < 0.10) over baseline NDM performance in mild cognitive impairment and AD groups using APOE, SORL1, and FGL2 for gene modulation. The results were robust and replicable when testing on an external cohort of the Alzheimer's Disease Sequencing Project. The study thus demonstrates the importance of regional genetic vulnerability, in conjunction with network diffusion mechanisms, in improving the modelling and prediction of amyloid- β pathophysiological spread.
Forest plantations cover large areas globally, but their climate benefits remain unclear, hampering projections of the future land carbon (C) sink. Here, we combined an unprecedented 14-years series of eddy-covariance CO2 fluxes (net ecosystem productivity, NEP) with biometric measurements to explore C balance in a commercial Eucalyptus plantation in Brazil across three rotations. The plantation exhibited a 14-year average and maximum annual NEP of 9.8 and 20.2 MgC ha-1 y-1, respectively, which ranks among the highest documented forest productivity. The amount of time before the forest recaptures as much C as was emitted after harvest was substantially shorter than previously published values: 20 and 27 months, for first and second harvest, respectively. Time series of leaf area index, but not trunk biomass growth, were strongly related to NEP dynamics (> 75% of explained variance at annual and monthly scales) suggesting possible amenability to remote-sensing assessment in Eucalyptus plantations. Estimates of C accumulation in litter and below-ground stocks varied from positive to negative depending on the methodology used, and showed substantial differences across rotations. This indicates that long-term C accumulation in commercial Eucalyptus plantations is not guaranteed, despite their high productivity.
Studying tick behavior is crucial for understanding how climate, disturbances, and land-use changes shape tick populations and tick-borne disease risk. Mark-release-recapture studies can provide valuable answers to questions regarding tick movement and behavior, population sizes, and survivorship.Standard tick mark-release-recapture provides limited resolution to understanding individual behaviors, limiting our ability to answer questions that require repeated observations of the same individuals. We developed a new, operationally simple method to track large populations of individual ticks over space and time.We found non-random movement patterns, including directed movement towards grass, vegetation-dependent dispersal distances and rate, and sex-based differences in movement. Practical implication: This method can be applied to other tick species to assess tick longevity, determine dispersal ranges and rate, and analyze questing behavior and success.
Materials exhibiting mechanoluminescence (ML) that directly convert mechanical stimuli into light hold significant potential for real-time stress sensing and intelligent photonic systems. However, most high-performance ML systems rely on complex multicomponent compounds that often suffer from limited intensity, stability, and scalability, largely due to poorly understood mechanisms. Herein, we report a simple Al2O3:Cr3+ oxide that exhibits unprecedented ML intensity, enabled by a well-defined mechanical-to-optical energy conversion process. The self-recoverable ML arises from stress-induced ionization of electrons from luminescence centers, followed by their recapture upon stress release. By precisely tuning the doping levels, annealing conditions, and heterojunction interfaces, Al2O3:Cr3+ phosphors achieved intense, reproducible, and thermally stable near-infrared emission. Notably, high-temperature annealing dramatically enhanced the ML intensity, with thermodynamic and kinetic analyses revealing increases in the carrier and defect concentrations by several orders of magnitude, accounting for the exceptional brightness. By leveraging the chemical robustness, abundance, and low cost of alumina, we demonstrated the flexible ML paper for stress visualization and multi-level anti-counterfeiting, as well as in-situ grown Al2O3:Cr3+ luminescent layers on Cr-Al alloys for passive, real-time stress monitoring. This study establishes Al2O3 as a durable and scalable oxide platform for next-generation self-recoverable ML materials, bridging fundamental research and practical sensing technologies.
Survivorship (or selection) bias arises within statistical analyses where the observed data are subject to some underlying selection process prior to entry into the sampled data. For example, within capture-recapture studies, a primary selection mechanism is the survival until initial capture time. The common Cormack-Jolly-Seber model conditions on the first time an individual is observed, leading to potential survivorship bias. However, while the issue of survivorship bias has been well studied in many fields, there has been little exploration within the capture-recapture framework. In particular, we focus on individual (continuous) random effect Cormack-Jolly-Seber models, where it is assumed that individuals have different survival probabilities, specified to be from some common underlying distribution. We discuss the implications of the survivorship bias within the data collection process, and describe a novel modeling approach that accounts for the survivorship bias within an ecologically sensible manner. Using simulated data, we demonstrate the significant impact of ignoring the survivorship bias present in the data. We fit the corrected model to a guillemot data set and demonstrate that even with relatively mild selection bias, the individual heterogeneity variability is substantially underestimated when ignoring this survivorship bias.
Climate change is causing measurable harm globally1,2. Political and legal efforts seek to link these damages with specific emissions, including in discussions of loss and damage (L&D)3,4; however, no quantitative definition of L&D exists5,6, nor is there a framework to link past and future emissions from specific sources to monetized, location-specific damages. Here we develop such a framework, which is integrated with recent efforts to estimate the social cost of carbon7. Using empirical estimates of the non-linear relationship between temperature and aggregate economic output, we show that future damages from past emissions-one component of L&D-are at least an order of magnitude larger than historical damages from the same emissions. For instance, one tonne of CO2 emitted in 1990 caused US$180 in discounted global damages by 2020 ($40-530) and will cause an additional $1,840 through 2100 ($500-5,700). Thus, settling debts for past damages will not settle debts for past emissions. In other illustrative estimates, a single long-haul flight per year over the past decade leads to about $25k ($6,000-77,000) in future damages by 2100, and US emissions since 1990 caused $500 billion ($180-1,300 billion) of damage in India and $330 billion ($110-820 billion) in Brazil. Carbon removal offers an alternative to transfer payments for settling L&D, but is increasingly ineffective in limiting damages as the delay between emission and recapture increases.
Mammalian thyroid status is governed by thyroid secretion of L-thyroxine (T4) as a prohormone that is monodeiodinated in peripheral tissues to bioactive T3 (3,5,3'-triiodo-l-thyronine). T4 secretion is controlled by the hypothalamic-pituitary-thyroid (HPT) axis (central control) whereas T3 availability to target cells depends mainly on mechanisms in extrathyroidal tissues such as cellular transport and deiodination (peripheral control). Does this model apply to poikilothermic teleost fish which in contrast to homeothermic mammals may show major surges in plasma T4 due to season, feeding, reproductive state or stressors? We have evaluated the contributions of central and peripheral mechanisms to fish thyroid status in light of recent discoveries employing both traditional endocrine approaches and more modern molecular biological techniques, focusing primarily on salmonid species which may undergo a unique thyroid-implicated premigratory parr-smolt transition (PST), and which as tetraploids may express multiple paralogs of regulatory peptides. Most teleost research has focused on peripheral control by the three classic deiodinases (D1, D2 and D3). In salmonids they determine systemic (D1, D2) and tissue-specific (D2) T3 generation from T4 and the equally critical T4 and T3 degradations (D1, D3). Tetraploid salmonids may express up to four paralogs for a given deiodinase, providing the potential for species-specific or tissue-specific T3 production, curtailment of T3 action, or iodine recapture. Critical as they appear, salmonid deiodinases do not function in isolation but in concert with, and dependence on, TH plasma transport, cell-membrane translocation, hepatic conjugation, biliary excretion and gastrointestinal metabolism. Two rainbow trout properties are particularly distinct from the mammalian model: i) T3, but not T4, exchanges rapidly between plasma and erythrocytes permitting plasma T3 stability despite marked acute changes in plasma T4 and ii) in contrast to ingested T4, which is unavailable from food due to complete gastrointestinal deiodination, ingested T3 contributes to the plasma T3 pool. Thus the teleost liver, poised at the confluence of exogenous and endogenous T3 sources, may play a strategic role through its TH biliary excretion, deiodination and other pathways in regulating systemic T3 availability involved in anabolic/catabolic balance and somatic growth. A major consequence of ingested T4 degradation is the exclusive delegation of T4 availability to the HPT axis. Since mammalian TSH consistently stimulates teleost T4 secretion a mammal-like HPT central control model has been assumed. However, teleost HPT function differs from that of homeotherms in both its hypothalamic control and response to external stimuli. T4 secretion could be regulated mainly by T4 negative feedback with the HPT axis playing a subsidiary role of merely ensuring adequate T4 substrate for regulated peripheral deiodination to proceed. However, this does not account for the notable surges in salmonid plasma T4 and implies resetting of the HPT 'thyrostat'. Thus the role of central TSH control in the regulation of plasma T4 changes remains unclear, awaiting further characterization of endogenous TSH secretion. Furthermore, discoveries of TSH-subunit and TSH-receptor expression in piscine peripheral tissues such as the CNS, liver, and gonad require reassessment of TSH function with a focus not only on its traditional endocrine actions but also on its potential as a paracrine regulator of TH action in peripheral tissues. In conclusion, while there are many similarities in thyroid regulation between mammals and salmonids there are also key differences. These likely stem from the evolution of homeothermy, the constraints of terrestrial iodine availability and a plasticity in salmonid peripheral and central control resulting from tetraploidy.
Photoresponsive hydrogen-bonded azo-macrocycles capable of selectively recognizing lithium cation were constructed by reversing the amide-azobenzene connectivity, which redistributes electron density and preorganizes four carbonyl oxygen donors into a smaller, more convergent cavity. Compared with a connectivity-isomeric reference macrocycle, the new receptor displays a pronounced preference for Li+, in which complexation with LiClO4 shows a slow exchange on the 1H NMR timescale and an association constant (Ka) exceeding 104 M-1, whereas the reference binds Li+ weakly (<5 M-1). In contrast, both hosts exhibit only modest binding toward Na+ (102~103 M-1) and fast exchange, consistent with size/geometry matching of the compressed cavity to Li+. The newly designed azo-macrocycles reveal a highly selective recognition of Li+ thanks to the more evenly arrayed four amide oxygens enclosing a cavity of small dimension. Notably, E/Z photoisomerization of macrocycle switches the binding regime, enabling reversibly light-triggered Li+ binding under UV irradiation and recapture under visible light. This work establishes a new photoresponsive receptor based on H-bonded azo-macrocycles for photoswitchable recognition of Li+.
Chimpanzee populations have declined severely in recent decades because of habitat loss and illegal hunting, emphasizing the need to monitor population density to support conservation. Since 1992, the Jane Goodall Institute has led chimpanzee conservation efforts in the Republic of Congo through the Tchimpounga Chimpanzee Rehabilitation Center and, more recently, the Tchimpounga Nature Reserve (TNR), in partnership with the Ministry of Forest Economy. The abundance of central chimpanzees (Pan troglodytes troglodytes) in the country is largely unknown, with existing studies restricted to conservation projects in Nouabale-Ndoki, Odzala-Kokua, and Conkouati-Douli National Parks. In 2019, we conducted a study in the TNR to estimate chimpanzee density using Spatially Explicit Capture-Recapture (SECR) analysis based on camera trap data. We compared SECR estimates with non-spatial capture-recapture (CR) and the distance sampling standing-crop nest count method. Our results show that SECR provided the most reliable estimates in terms of precision and cost-effectiveness. Over 6 months, 116 chimpanzees were identified across 17 of 28 camera traps deployed in a 252 km² grid. SECR estimated a density of 0.87 chimpanzees/km² (95% CI: 0.58-1.30), while distance sampling produced a similar estimate of 0.81 chimpanzees/km² (95% CI: 0.43-1.53). This study is the first to apply SECR to great apes in the Republic of Congo and provides a foundation for future research and improved conservation management in the TNR.
Characterizing age-structure patterns in amphibian populations is crucial to unravel the drivers of their demographic dynamics and implement biologically informed conservation management. Consequently, accurately assessing the age of individuals in wild populations is of utmost importance in the face of the current amphibian crisis. However, age estimation in amphibians has so far remained elusive due to the difficulty of lifetime tracking, especially during the terrestrial juvenile stage, and to the uncertainty associated with alternative methods like skeletochronology. Here, we illustrate the usefulness of long-term monitoring programs based on capture-mark-recapture to address age estimation from body size data. Specifically, we combined repeated body size measurements of marked individuals of unknown age throughout their adult life with growth records of individuals marked as postmetamorphic juveniles and recaptured years later as sexually mature adults, focusing on 10 amphibian species in central Spain. Growth models fitted to mark-recapture data provided robust estimates of size-at-age, supported by data on individuals of known age for five of the study species. For three additional species, known age records disproved mark-recapture models, but rendered enough data to build alternative growth models. The inferred consensus size estimates allowed discerning first- and second-year age classes in some species, contributing to assess the age at maturity of individuals and population-specific patterns of recruitment. Consequently, our demographic approach provided multi-evidence information to address age-at-size estimation in amphibians, thus demonstrating the value of long-term mark-recapture programs to fill pervasive gaps in our knowledge about amphibian demographic dynamics.
Transpired solar collectors (TSCs) preheat ventilation air and reduce conductive heat loss through the building envelope, thereby acting as dynamic insulation. Direct outdoor comparisons of glazed and unglazed TSC modules operating under identical conditions remain limited. A systematic experimental study was conducted on two façade-mounted modules with the same perforated galvanised-steel absorber and 110 mm plenum: an unglazed TSC (UTSC) and a glazed TSC (GTSC) fitted with 4 mm low-iron tempered glass. The system was tested outdoors in Amman, Jordan, under clear-sky conditions (January-March 2025) at specific airflow rates of 18-144 m3/(h·m2) and solar irradiance of 200-1000 W/m2. Passive natural-convection operation of the GTSC was also evaluated at several cavity heights. Performance was quantified using physically bounded metrics covering thermal performance, heat exchange, exergy, wall heat-loss recapture, ventilation load reduction, and economic return. Across the tested range, the GTSC achieved thermal efficiency of 48-75% and exergy efficiency up to 12%, outperforming the UTSC (42-65%) by 6-9% points. The UTSC wall heat-loss recapture index decreased from 93% at low irradiance to 63% at peak irradiance, consistent with dynamic-insulation behaviour, while GTSC ventilation load reduction increased monotonically from 60% to 90%. Under passive operation, the GTSC reached 55% efficiency at 3.0 m cavity height without fan energy. Economic assessment using Jordanian energy prices indicated payback periods of 4.4 years (UTSC) and 5.2 years (GTSC). The results provide experimentally grounded guidance on when glazing is justified for sustainable ventilation preheating in buildings.
Capture-recapture methods estimate the size of an elusive population based on repeated partial observations. In closed populations, estimators are typically constructed by modeling either the probability of capture frequencies or entire capture histories of observed units. This paper introduces a survey sampling approach to capture-recapture modeling. By framing capture occasions as sample draws, we begin with an explicit assumption on the sampling design, such as simple random sampling or, in the case of this paper, PPS sampling, allowing population size estimators to be tailored to the data collection methods of a given experiment or study. We model individual inclusion probabilities, accommodating latent heterogeneity, and variable sample sizes-the latter of which is often overlooked in current research-and show that the resulting estimator shares many similarities with current methods. To evaluate the proposed estimator, we present a novel simulation framework that generates unequal probability samples, providing unique insights into the estimator's performance under varying sampling effort and heterogeneity assumptions. The results of our simulations and applications to 14 benchmark datasets demonstrate that our method performs competitively, matching the accuracy of well-established estimators.
To develop and validate an artificial intelligence (AI) model for classifying infectious keratitis (IK) etiologies from anterior segment images. Retrospective development and validation study of an artificial intelligence diagnostic system. A total of 708 anterior segment images were collected, comprising Acanthamoeba (n = 159), bacterial (n = 139), fungal (n = 188), and viral (n = 222) cases. Of these, 628 images were used to train three convolutional neural network architectures (DenseNet-121, ResNet-50, and EfficientNet-B6) using stratified 5-fold cross-validation. Model performance was then assessed on an independent 80-image test set, with diagnostic accuracy benchmarked against the averaged diagnoses of 33 board-certified ophthalmologists. The validated model was subsequently integrated into a smartphone application. EfficientNet-B6 achieved the highest performance among tested architectures, achieving a mean accuracy of 61.5% (95% CI: 57.3-65.7%) across pathogen categories and significantly outperforming ophthalmologists, whose mean accuracy was 39.1% (95% CI: 36.4-41.8%). Receiver Operating Characteristic analysis was performed using the model from the best-performing cross-validation fold, evaluated on the independent 80-image test set. The analysis showed Area Under the Curve (AUC) values of 0.84 for AK, 0.84 for BK, 0.88 for FK, and 0.87 for VK. The smartphone-based application incorporating our AI model demonstrated a diagnostic accuracy comparable to that of the computer-based system when the same test dataset was re-captured from a monitor using the smartphone camera, suggesting successful translation of the algorithm to mobile platforms. Our smartphone-based AI system demonstrates moderate-to-good diagnostic performance for identifying IK pathogens using diffuser-anterior segment photography, thereby enabling practical implementation via smartphone cameras.
Research Highlight: Ponchon, A., Choquet, R., Martins, A., Ruiz-Miranda, C., Albert, C., & Romano, V. (2026). Yellow fever outbreak temporarily changes dispersal patterns in an endangered primate. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.70250. Infectious diseases can be drivers of wildlife population dynamics, but how they reshape host movement and social organization remains poorly understood. Ponchon et al. (2026) present one of the rare examples where the impact of a disease outbreak on survival can be measured across a population of wild mammals and subsequently linked to behavioural changes of these animals. They use a unique opportunity by tracking the survival and dispersal decisions of golden lion tamarins (Leontopithecus rosalia) during a sylvatic yellow fever outbreak. Using a 13-year capture-recapture dataset, the authors show that the outbreak caused a temporary decline in adult survival and triggered scale-dependent behavioural responses. While local dispersal within forest fragments decreased, the probability of long-distance dispersal across the matrix increased tenfold. At the same time, group sizes declined during the outbreak, while the number of social groups remained stable and increased after the outbreak. These results indicate that recovery did not involve a simple return to pre-outbreak conditions, but rather a reorganization of social structure characterized by smaller and more numerous groups. These findings highlight that, in vector-borne systems, movement may offer only limited protection against infection and demonstrate that disease outbreaks can lead to lasting population-wide changes in group sizes.
Age at first reproduction is an important life-history trait that marks the beginning of reproductive allocation in long-lived organisms and drives patterns of life-history strategies. Demographic factors and environmental conditions likely affect age at first reproduction through multiple pathways: food resources availability and energy storage from birth to recruitment, competition for breeding sites and mate availability. Using a unique 35-year dataset of individual-based mark-recapture data from a wandering albatross (Diomedea exulans) population at Crozet (southern Indian Ocean), we investigated how demographic factors and environment influence age at first reproduction. The population experienced major fluctuations, declining by 50% in the 1970s before partially recovering in the 1980s. It was also exposed to important environmental changes, including variations in large-scale climate phenomena and changes in subtropical anticyclone systems like the Mascarene high pressure system. We used multi-event hidden Markov models to estimate age-specific survival and breeding probabilities for each sex separately. From these models, we estimated the age at first reproduction through absorbing Markov chains while accounting for imperfect detection. We investigated how demographic factors (population density at birth and mate availability at recruitment) and environmental conditions (at birth and recruitment) influenced age at first reproduction through their effects on survival and breeding probabilities. Age at first reproduction declined across cohorts for both sexes from 1970 to the mid-1980s, then stabilized. Females recruited at 9.0 years in early cohorts versus 7.5 years in later ones; males declined from 10.2 to 9.2 years. Environmental conditions at birth, particularly the El Niño Southern Oscillation and the Mascarene high, influenced recruitment timing through delayed effects of natal condition on breeding probability rather than survival. Mate availability strongly facilitated earlier recruitment in both sexes, while natal population density delayed male recruitment specifically. Recruitment timing in wandering albatrosses is shaped primarily by developmental programming during the natal period rather than by immediate environmental triggers at sexual maturity, with mate availability and population density modulating these early-life effects in sex-specific ways. Given that recruitment is an important life-history event linked to population-level reproductive rates, accurate demographic projections require models accounting for cohort-specific effects under changing environments.
Invasive species management demands predictive models that balance accuracy with ecological interpretability, yet traditional approaches often fail to capture complex environmental interactions. We evaluated hybrid frameworks integrating biological and machine learning models for rainbow trout (Oncorhynchus mykiss) growth in the Lower Colorado River using ten years of tag-recapture data and environmental covariates, comparing traditional and Bayesian von Bertalanffy (VBGM) and Gompertz models with Random Forests, XGBoost, LightGBM, Support Vector Regression, Neural Networks, and ensemble methods through probabilistic performance analysis. Incorporating environmental context and advanced modeling produced substantial gains, with top methods achieving 70-80 percent error reductions relative to baseline models, equivalent to 45-70 mm or 20-32 percent of mean fish length. A stacked ensemble of XGBoost and the VBGM achieved the best performance (RMSE = 15.96 mm, [Formula: see text]) and exhibited stochastic dominance across the posterior, while gradient boosting models formed a strong second tier, led by LightGBM and XGBoost. Bayesian Model Averaging reached comparable accuracy while explicitly quantifying uncertainty. Even traditional mechanistic models improved by up to 80 percent when enhanced with covariates and Bayesian estimation, preserving biological interpretability through parameters such as asymptotic size and growth rate. Feature importance analysis identified initial length, time at large, and weight at release as dominant predictors, and the stacked ensemble outperformed baseline models in over 99 percent of posterior samples. These results establish hybrid ensemble frameworks as powerful tools for ecological forecasting that unite predictive performance with mechanistic insight, providing a generalizable template for systems where both accuracy and interpretability are required.
There is limited evidence to support optimal concentrations for therapeutic drug monitoring in patients with Crohn's disease (CD) who experience a loss of response (LOR) to tumour necrosis factor antagonists. This study aimed to determine the threshold trough adalimumab concentration beyond which patients with LOR are unable to recapture a response after dose escalation. Enrolled patients had responded to adalimumab therapy after ≥16 weeks and subsequently experienced a secondary LOR, defined as a C-reactive protein (CRP) level ≥5 mg/L and/or a fecal calprotectin (FC) level ≥250 µg/g. Dosing was escalated from biweekly to weekly. Patients were then assessed for 12 weeks to determine their ability to recapture a response, defined as a CRP response (≥50% CRP decrease and/or CRP <5 mg/L) and/or FC response (≥50% FC decrease and/or FC <150 µg/g). The relationship between baseline trough concentration and non-recapture of biochemical response was evaluated using logistic regression. Of 97 enrolled patients, 49 (50.5%) did not recapture a response to adalimumab after dose escalation. Baseline trough concentration was not associated with non-recapture of biochemical response (odds ratio, 0.98; 95% CI: 0.91-1.06; P = .626). There was no threshold trough concentration identified that was predictive of non-recapture of biochemical response. No new adalimumab safety signals were identified. No association was observed between trough adalimumab concentration and non-recapture of biochemical response after dose escalation for secondary LOR in CD. The threshold concentration above which dose escalation is ineffective remains unclear. ClinicalTrials.gov identifier: NCT02896985.
Individual identification is essential for wildlife population research but commonly relies on invasive methods that may negatively affect animal welfare. This study proposes a safe, simple, and rapid non-invasive identification method for the endangered Mongolian racerunner (Eremias argus) based on coding dorsal spot patterns. We documented 175 individuals using photographic data and assigned unique identification codes derived from two central longitudinal spot lines and overlapping spots, which capture individual-specific traits. No individuals were found to share identical codes during identification. Recaptured individuals showed stable spot patterns without any code changes, confirming the method's reliability for long-term monitoring. This approach requires only standard cameras or smartphones and is thus practical for field surveys and citizen science initiatives. The proposed method minimizes impacts on target species while maintaining high accuracy, offering a valuable alternative for ethical conservation and population studies.
Dispersal is an important mechanism linked with population viability. Increases in species-specific dispersal allow for improved connectivity between habitat patches and populations. Here, we seek to understand the role of both biotic and abiotic factors, and their interactions, in influencing the movement of the recently identified and federally threatened Rocky Mountain sculpin (Cottus sp.). We conducted a mark-recapture study in a 400 m reach of Lee Creek in Alberta, Canada, using passive integrated transponder and visible implant elastomer tags across approximately 4 months. Boosted regression tree models were used to assess the movement of (1) all recaptured individuals (global model) and (2) only mobile individuals (movement only model) in response to abiotic and biotic factors. Biotic factors, such as congeners at the destination (8.7%), congeners at the origin (8.0%), and competitors at the origin (7.2%) were the most important variables for predicting movement in the global model. Alternatively, cobble (18.7%) followed by biotic factors including congeners at the origin (9.6%) and competitors at the origin (9.1%) were the most important variables selected in the movement only model. Biotic and abiotic factors showed strong interactions, providing a clear example of the importance of competition in the understanding of movement. Although the vast majority of restoration activities for endangered species are aimed at abiotic (i.e. habitat-related) factors, this study shows how these may be limited without considering biotic interactions, such as the role of inter- and intraspecific competition. The online version contains supplementary material available at 10.1007/s10641-026-01841-9.